HumanHeartProject - Feature Plots
I. Hanka
2025-09-24
load packages
load object All
fileNam <- "/Users/immbio/Desktop/HumanHeartCarTrans2/data/Human_heart_allmerged_seurat.rds"
seuratM <- readRDS(fileNam)seuratM$clusterName <- "clusterName"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "0" )] <- "Fb1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "1" )] <- "PerivFb1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "2" )] <- "Mph2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "3" )] <- "BEC1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "4" )] <- "Fb2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "5" )] <- "CM"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "6" )] <- "Tcell1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "7" )] <- "BEC2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "8" )] <- "VSMC"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "9" )] <- "Mph1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "10" )] <- "BEC3"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "11" )] <- "NC"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "12" )] <- "BaroRec"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "13" )] <- "Bcell"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "14" )] <- "Fb3"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "15" )] <- "Tcell2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "16" )] <- "LEC"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "17" )] <- "PerivFb2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "18" )] <- "Adipoc"
colclusterName <- c("#67001f", "#f4a582","#D53E4F", "#B45B5C","#003c30","#01665e","#66C2A5", "#BEAEF8","#BEAED4", "#c7eae5", "#B09C85", "#4e5a4c","#393A3F","pink","#4588CA","#3299CA","#FCC80B","#FEE60B","#628395")
names(colclusterName) <- c("CM","Fb1","Fb2","Fb3","PerivFb1","PerivFb2","VSMC","BEC1","BEC2","BEC3","LEC","NC","BaroRec","Adipoc","Mph1","Mph2","Tcell1","Tcell2","Bcell")
coldiseaseCond <- c("#dfc27d","#BE3144","#f4a582","#B45B5C","#8c510a","#202547","#355C7D","#779d8d", "#01665e", "#3288BD", "#BEAED4")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2", "visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")plot features
##Fb
FeaturePlot(seuratM, features = "ENSG00000026025.VIM", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000163359.COL6A3", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000091136.LAMB1", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000011465.DCN", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000196569.LAMA2", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000011465.DCN", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000071991.CDH19", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
##PerivFb/VSMC
FeaturePlot(seuratM, features = "ENSG00000113721.PDGFRB", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000143248.RGS5", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000076706.MCAM", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000135424.ITGA7", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000107796.ACTA2", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
##CM
FeaturePlot(seuratM, features = "ENSG00000197616.MYH6", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000078814.MYH7B", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000134571.MYBPC3", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000092054.MYH7", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000198626.RYR2", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000078114.NEBL", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000175084.DES", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000155657.TTN", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000118194.TNNT2", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
#B cells
FeaturePlot(seuratM, features = "ENSG00000177455.CD19", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE)
FeaturePlot(seuratM, features = "ENSG00000012124.CD22", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE) #mature Bcells
FeaturePlot(seuratM, features = "ENSG00000143297.FCRL5", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE) #Plasmacells
FeaturePlot(seuratM, features = "ENSG00000156738.MS4A1", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE) #CD20, mature Bcells
FeaturePlot(seuratM, features = "ENSG00000211592.IGKC", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE) 
FeaturePlot(seuratM, features = "ENSG00000211899.IGHM", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE) 
FeaturePlot(seuratM, features = "ENSG00000196092.PAX5", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE) 
#T cells
FeaturePlot(seuratM, features = "ENSG00000198851.CD3E", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE) 
FeaturePlot(seuratM, features = "ENSG00000010610.CD4", pt.size = 0.5, cols = c("lightgrey", "#BE3144"), raster = FALSE) 
sel features
genes <- data.frame(gene=rownames(seuratM)) %>%
mutate(geneID=gsub("^.*\\.", "", gene))
selGenes <- data.frame(geneID=c("ACTA2", "MYH6", "COL1A1", "MYH11")) %>%
left_join(., genes, by="geneID")
pList <- sapply(selGenes$gene, function(x){
p <- FeaturePlot(seuratM, features = x, reduction = "umap", pt.size = 0.1, cols = c("lightgrey", "#BE3144"), raster = FALSE) +
theme(legend.position="right")
plot(p)
})
dotplot inflammatory genes
seuratM$clusterName <- factor(seuratM$clusterName, levels = c("CM","Fb1","Fb2","Fb3","PerivFb1","PerivFb2","VSMC","BEC1","BEC2","BEC3","LEC","NC","BaroRec","Adipoc","Mph1","Mph2","Tcell1","Tcell2","Bcell"))
Idents(seuratM) <- seuratM$clusterName
genes <- data.frame(gene=rownames(seuratM)) %>%
mutate(geneID=sub("^.*\\.", "", gene))
# Create selGenes with geneID vector, join, and filter
selGenes <- data.frame(geneID = rev(c("IL1B", "IL1R1", "IL6", "IL6R", "TNF", "TNFRSF1A", "TNFRSF1B", "IL18", "IL18R1", "IL15", "IL15RA", "IFNG", "IFNGR1", "IFNG-AS1", "IFNAR1", "IFNAR2", "IFNA5"))) %>%
left_join(., genes, by = "geneID")
# Run dot plot
dittoSeq::dittoDotPlot(seuratM, vars = selGenes$gene, group.by = "clusterName") + RotatedAxis() + coord_flip() + scale_color_viridis(option= "F")
BMP features
genes <- data.frame(gene=rownames(seuratM)) %>%
mutate(geneID=gsub("^.*\\.", "", gene))
selGenes <- data.frame(geneID=c("BMPER")) %>%
left_join(., genes, by="geneID")
pList <- sapply(selGenes$gene, function(x){
p <- FeaturePlot(seuratM, features = x, reduction = "umap", pt.size = 0.1, cols = c("lightgrey", "#BE3144"), raster = FALSE) +
theme(legend.position="right")
plot(p)
})
session info
date()## [1] "Tue Sep 30 12:05:04 2025"sessionInfo()## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Zurich
## tzcode source: internal
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] NCmisc_1.2.0 VennDiagram_1.7.3 futile.logger_1.4.3
## [4] ggupset_0.4.1 gridExtra_2.3 DOSE_4.2.0
## [7] enrichplot_1.28.4 msigdbr_25.1.1 org.Hs.eg.db_3.21.0
## [10] AnnotationDbi_1.70.0 clusterProfiler_4.16.0 multtest_2.64.0
## [13] metap_1.12 scater_1.35.0 scuttle_1.18.0
## [16] destiny_3.22.0 circlize_0.4.16 muscat_1.22.0
## [19] viridis_0.6.5 viridisLite_0.4.2 lubridate_1.9.4
## [22] forcats_1.0.1 stringr_1.5.2 purrr_1.1.0
## [25] readr_2.1.5 tidyr_1.3.1 tibble_3.3.0
## [28] tidyverse_2.0.0 dplyr_1.1.4 SingleCellExperiment_1.30.1
## [31] SummarizedExperiment_1.38.1 Biobase_2.68.0 GenomicRanges_1.60.0
## [34] GenomeInfoDb_1.44.3 IRanges_2.42.0 S4Vectors_0.46.0
## [37] BiocGenerics_0.54.0 generics_0.1.4 MatrixGenerics_1.20.0
## [40] matrixStats_1.5.0 pheatmap_1.0.13 ggpubr_0.6.1
## [43] ggplot2_4.0.0 Seurat_5.3.0 SeuratObject_5.2.0
## [46] sp_2.2-0
##
## loaded via a namespace (and not attached):
## [1] igraph_2.1.4 ica_1.0-3 plotly_4.11.0
## [4] Formula_1.2-5 tidyselect_1.2.1 bit_4.6.0
## [7] doParallel_1.0.17 clue_0.3-66 lattice_0.22-7
## [10] rjson_0.2.23 blob_1.2.4 S4Arrays_1.8.1
## [13] pbkrtest_0.5.5 parallel_4.5.1 png_0.1-8
## [16] plotrix_3.8-4 cli_3.6.5 ggplotify_0.1.3
## [19] goftest_1.2-3 VIM_6.2.6 variancePartition_1.38.1
## [22] BiocNeighbors_2.2.0 uwot_0.2.3 curl_7.0.0
## [25] mime_0.13 evaluate_1.0.5 tidytree_0.4.6
## [28] ComplexHeatmap_2.24.1 stringi_1.8.7 backports_1.5.0
## [31] lmerTest_3.1-3 qqconf_1.3.2 httpuv_1.6.16
## [34] magrittr_2.0.4 rappdirs_0.3.3 splines_4.5.1
## [37] sctransform_0.4.2 ggbeeswarm_0.7.2 DBI_1.2.3
## [40] jquerylib_0.1.4 smoother_1.3 withr_3.0.2
## [43] corpcor_1.6.10 reformulas_0.4.1 class_7.3-23
## [46] lmtest_0.9-40 formatR_1.14 htmlwidgets_1.6.4
## [49] fs_1.6.6 ggrepel_0.9.6 labeling_0.4.3
## [52] fANCOVA_0.6-1 SparseArray_1.8.1 DESeq2_1.48.2
## [55] ranger_0.17.0 DEoptimR_1.1-4 reticulate_1.43.0
## [58] hexbin_1.28.5 zoo_1.8-14 XVector_0.48.0
## [61] knitr_1.50 ggplot.multistats_1.0.1 UCSC.utils_1.4.0
## [64] RhpcBLASctl_0.23-42 timechange_0.3.0 foreach_1.5.2
## [67] dittoSeq_1.20.0 patchwork_1.3.2 caTools_1.18.3
## [70] data.table_1.17.8 ggtree_3.16.3 R.oo_1.27.1
## [73] RSpectra_0.16-2 irlba_2.3.5.1 fastDummies_1.7.5
## [76] gridGraphics_0.5-1 lazyeval_0.2.2 yaml_2.3.10
## [79] survival_3.8-3 scattermore_1.2 crayon_1.5.3
## [82] RcppAnnoy_0.0.22 RColorBrewer_1.1-3 progressr_0.16.0
## [85] later_1.4.4 ggridges_0.5.7 codetools_0.2-20
## [88] GlobalOptions_0.1.2 aod_1.3.3 KEGGREST_1.48.1
## [91] Rtsne_0.17 shape_1.4.6.1 limma_3.64.3
## [94] pkgconfig_2.0.3 TMB_1.9.17 spatstat.univar_3.1-4
## [97] mathjaxr_1.8-0 EnvStats_3.1.0 aplot_0.2.9
## [100] scatterplot3d_0.3-44 spatstat.sparse_3.1-0 ape_5.8-1
## [103] xtable_1.8-4 car_3.1-3 plyr_1.8.9
## [106] httr_1.4.7 rbibutils_2.3 tools_4.5.1
## [109] globals_0.18.0 beeswarm_0.4.0 broom_1.0.10
## [112] nlme_3.1-168 lambda.r_1.2.4 assertthat_0.2.1
## [115] lme4_1.1-37 digest_0.6.37 numDeriv_2016.8-1.1
## [118] Matrix_1.7-4 farver_2.1.2 tzdb_0.5.0
## [121] remaCor_0.0.20 reshape2_1.4.4 yulab.utils_0.2.1
## [124] glue_1.8.0 cachem_1.1.0 polyclip_1.10-7
## [127] Biostrings_2.76.0 mvtnorm_1.3-3 parallelly_1.45.1
## [130] mnormt_2.1.1 statmod_1.5.0 RcppHNSW_0.6.0
## [133] ScaledMatrix_1.16.0 carData_3.0-5 minqa_1.2.8
## [136] pbapply_1.7-4 spam_2.11-1 gson_0.1.0
## [139] gtools_3.9.5 ggsignif_0.6.4 RcppEigen_0.3.4.0.2
## [142] shiny_1.11.1 GenomeInfoDbData_1.2.14 glmmTMB_1.1.12
## [145] R.utils_2.13.0 memoise_2.0.1 rmarkdown_2.29
## [148] scales_1.4.0 R.methodsS3_1.8.2 future_1.67.0
## [151] RANN_2.6.2 spatstat.data_3.1-8 rstudioapi_0.17.1
## [154] cluster_2.1.8.1 mutoss_0.1-13 spatstat.utils_3.2-0
## [157] hms_1.1.3 fitdistrplus_1.2-4 cowplot_1.2.0
## [160] colorspace_2.1-2 rlang_1.1.6 xts_0.14.1
## [163] dotCall64_1.2 ggtangle_0.0.7 laeken_0.5.3
## [166] mgcv_1.9-3 xfun_0.53 e1071_1.7-16
## [169] TH.data_1.1-4 iterators_1.0.14 abind_1.4-8
## [172] GOSemSim_2.34.0 treeio_1.32.0 futile.options_1.0.1
## [175] bitops_1.0-9 Rdpack_2.6.4 promises_1.3.3
## [178] RSQLite_2.4.3 qvalue_2.40.0 sandwich_3.1-1
## [181] fgsea_1.34.2 DelayedArray_0.34.1 proxy_0.4-27
## [184] GO.db_3.21.0 compiler_4.5.1 prettyunits_1.2.0
## [187] boot_1.3-32 beachmat_2.24.0 listenv_0.9.1
## [190] Rcpp_1.1.0 edgeR_4.6.3 BiocSingular_1.24.0
## [193] tensor_1.5.1 MASS_7.3-65 progress_1.2.3
## [196] BiocParallel_1.42.2 babelgene_22.9 spatstat.random_3.4-2
## [199] R6_2.6.1 fastmap_1.2.0 multcomp_1.4-28
## [202] fastmatch_1.1-6 rstatix_0.7.2 vipor_0.4.7
## [205] TTR_0.24.4 ROCR_1.0-11 TFisher_0.2.0
## [208] rsvd_1.0.5 vcd_1.4-13 nnet_7.3-20
## [211] gtable_0.3.6 KernSmooth_2.23-26 miniUI_0.1.2
## [214] deldir_2.0-4 htmltools_0.5.8.1 ggthemes_5.1.0
## [217] bit64_4.6.0-1 spatstat.explore_3.5-3 lifecycle_1.0.4
## [220] blme_1.0-6 S7_0.2.0 nloptr_2.2.1
## [223] sass_0.4.10 vctrs_0.6.5 robustbase_0.99-6
## [226] spatstat.geom_3.6-0 sn_2.1.1 ggfun_0.2.0
## [229] future.apply_1.20.0 bslib_0.9.0 pillar_1.11.1
## [232] gplots_3.2.0 pcaMethods_2.0.0 locfit_1.5-9.12
## [235] jsonlite_2.0.0 GetoptLong_1.0.5